Earthmover, a climate tech startup co-founded by Ryan Abernathey and Joe Hamman, is retooling its mission to focus on fast-changing weather and geospatial data rather than longer-term climate projections, aiming to become a foundational platform—akin to Snowflake—but for meteorological and spatial datasets. The startup’s core product is a data architecture built to handle large, complex multidimensional data (what geospatial folks call rasters, AI folks call tensors), and its tools help customers extract actionable insights from that data. Earthmover has landed over 10 paying customers (including insurance, energy, renewable firms) and secured $7.2 million in seed funding led by Lowercarbon Capital, with participation from Costanoa Ventures and Preston-Werner Ventures. Their platform runs on major public cloud providers as well as on-premises, relies heavily on open source software like Xarray, Pangeo, and Icechunk, and aims to reduce risk for customers by ensuring that data isn’t locked in, even if Earthmover pivots later.
Sources: Earthmover Blog, AInvest, TechCrunch
Key Takeaways
– Earthmover is pivoting from broadly climate-oriented data to more dynamic, frequently changing data like weather, fire risk, forecasts, etc., because that’s where urgency and immediate demand lie.
– Its strategy leans heavily on open source tools (Xarray, Pangeo, Icechunk) and cloud-plus-on-premises deployment, to ensure scalability, flexibility, and risk mitigation for customers.
– With $7.2M in seed funding and more than 10 paying customers already, Earthmover is positioning itself as a serious contender to streamline how firms access, store, and extract insight from large geospatial/weather datasets.
In-Depth
Earthmover’s recent pivot reflects a growing recognition in the technology and climate sectors: data that moves fast demands platforms that are built for velocity, not just long-horizon modeling. While climate projections remain important for policy, infrastructure, and global risk assessments, day-to-day decision-making—whether for energy generation, disaster response, insurance underwriting, wildfire management, or supply chain planning—relies heavily on weather and geospatial data that change hour by hour, day by day. That’s the gap Earthmover intends to fill.
At the heart of Earthmover’s offering is a data structure and supporting stack designed for what many find cumbersome in traditional systems: multidimensional “tensor” or “raster” data—massive arrays of values over space and time. These data types are integral to weather forecasting, satellite imagery, fire spread modeling, and other use cases where new observations arrive continuously. Earthmover isn’t just storing this data; it’s building tools so users can query, transform, visualize, and derive insights from it without needing a bespoke engineering team for every use case.
One major reason customers are showing interest is the alignment with risk management: many sectors are exposed to weather volatility and climate extremes, and having real-time or near-real-time, granular spatial data can mean the difference between manageable risk and catastrophic loss. Firms like renewable energy operators care deeply about forecasting supply and demand, which depends heavily on weather; insurers care about wildfire risk; energy trading desks want updated forecasts; all of these benefit from data platforms that are fast, reliable, well-architected, and open. Early adopters include insurance startups like Kettle and large multinational energy companies, suggesting that both nimble and large players see value.
Financially, the $7.2 million seed round—led by Lowercarbon Capital, with Costanoa Ventures and Preston-Werner Ventures participating—gives Earthmover runway to build out more tooling atop its core platform, scale operations, and improve performance. The open source foundation isn’t just a matter of philosophy; it’s also a hedge—if Earthmover were to change course or fail, customers would still have their data in usable, open formats. And by supporting both cloud providers (AWS, Google Cloud, Microsoft Azure) and on-premise setups, it caters to diverse customer needs around latency, security, regulatory compliance, cost, and control.
Challenges remain. Competing with incumbents or with large weather-data providers who have vast resources and decades of domain expertise isn’t easy. Also, the performance demands of real-time, spatially detailed weather data are high, and storage/compute costs, latency, and data quality all matter. But Earthmover’s strategy of focusing on urgency, leveraging open source, and emphasizing customer risk reduction seems smart. If the company can continue to add customers, improve its tooling, and scale efficiently, it may well become a foundational infrastructure layer in the rapidly growing weather-geospatial data ecosystem.